an IEXS initiative · Magallanes, Chile
Human judgment, structured as data for AI.
We design specialized datasets that capture how experts evaluate situations, compare options, and make decisions. Tailored data for training and evaluating artificial intelligence systems.
More data alone does not teach a model to make better decisions.
It learns from examples showing which response is better, which is acceptable, which fails, and why. That judgment often lives in experts’ experience and is rarely documented in a way a model can use.
Fi-Tun/12 designs and produces commissioned datasets that make this judgment structured, comparable, and evaluable.
Many variables and exceptions. Expert judgment brings them into focus.
- What we deliver
- Structured datasets with explicit criteria, expert-reviewed examples, and quality checks.
- What we are not
- Not an off-the-shelf dataset catalog, a generic consultancy, or an AI software product.
- Where it fits
- Teams building, training, fine-tuning, or evaluating models that need specialized data to address a specific behavior or problem.
Each delivery focuses on what still needs improvement.
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1
Define the problem
Identify a specific model behavior or evaluation challenge.
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2
Capture expert judgment
Gather and structure criteria provided by human experts.
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3
Deliver the dataset
Specialized datasets with quality controls.
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4
Iterate on observed results
Use observed results and errors to guide the next delivery.
Variables, exceptions, and signals to understand.
Identify patterns, discrepancies, and specific errors.
Focus the next delivery on what still needs improvement.
An IEXS project from Chilean Patagonia.
Fi-Tun/12 originated within IEXS Strategic Consulting to explore a specific question: how to turn professional experience and human judgment into structured, comparable, and evaluable data. Consulting identifies and analyzes problems; Fi-Tun/12 translates this knowledge into specialized datasets for artificial intelligence.
PROJECT / IEXS
human judgment → data
Magallanes, Chile
What does your model need to learn to evaluate better?
Tell us about a specific problem. We can explore whether a suitable dataset can be designed.